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Improved Lasso for genomic selection

机译：改良的套索用于基因组选择

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Empirical experience with genomic selection in dairy cattle suggests that the distribution of the effects of single nucleotide polymorphisms (SNPs) might be far from normality for some traits. An alternative, avoiding the use of arbitrary prior information, is the Bayesian Lasso (BL). Regular BL uses a common variance parameter for residual and SNP effects (BL1Var). We propose here a BL with different residual and SNP effect variances (BL2Var), equivalent to the original Lasso formulation. The lambda parameter in Lasso is related to genetic variation in the population. We also suggest precomputing individual variances of SNP effects by BL2Var, to be later used in a linear mixed model (HetVar-GBLUP). Models were tested in a cross-validation design including 1756 Holstein and 678 Montbeliarde French bulls, with 1216 and 451 bulls used as training data; 51 325 and 49 625 polymorphic SNP were used. Milk production traits were tested. Other methods tested included linear mixed models using variances inferred from pedigree estimates or integrated out from the data. Estimates of genetic variation in the population were close to pedigree estimates in BL2Var but not in BL1Var. BL1Var shrank breeding values too little because of the common variance. BL2Var was the most accurate method for prediction and accommodated well major genes, in particular for fat percentage. BL1Var was the least accurate. HetVar-GBLUP was almost as accurate as BL2Var and allows for simple computations and extensions.
机译：奶牛基因组选择的经验表明，单核苷酸多态性（SNPs）的影响分布可能与某些性状不正常。一种避免使用任意先验信息的替代方法是贝叶斯套索（BL）。常规BL对残差和SNP效应使用公共方差参数（BL1Var）。我们在这里提出一个具有不同残差和SNP效应方差（BL2Var）的BL，等效于原始的套索公式。套索中的lambda参数与种群中的遗传变异有关。我们还建议通过BL2Var预先计算SNP效应的个体方差，稍后将在线性混合模型（HetVar-GBLUP）中使用。在交叉验证设计中对模型进行了测试，包括1756 Holstein和678 Montbeliarde法国公牛，其中1216和451公牛用作训练数据。使用了51 325和49625多态性SNP。测试了牛奶的生产特性。测试的其他方法包括使用由谱系估计推断出的差异或从数据中整合出来的线性混合模型。群体中遗传变异的估计值与BL2Var中的谱系估计值相近，而BL1Var中的谱系估计值则与谱系估计值接近。由于存在共同差异，BL1Var缩小了育种值。 BL2Var是最准确的预测方法，可容纳主要基因，尤其是脂肪百分比。 BL1Var最不准确。 HetVar-GBLUP几乎与BL2Var一样准确，并且允许进行简单的计算和扩展。

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